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Recurrent neural network (RNN) based character-level language models (CLMs) are extremely useful for modeling out-of-vocabulary words by nature. However, their performance is generally much worse than the word-level language models (WLMs), since CLMs need to consider longer history of tokens to properly predict the next one. We address this problem by proposing hierarchical RNN architectures, which consist of multiple modules with different timescales.

This paper demonstrates the ability to accurately detect the movement state of Madagascar hissing cockroaches equipped with a custom board containing a five degree of freedom inertial measurement unit. The cockroach moves freely through an unobstructed arena while wirelessly transmitting its accelerometer and gyroscope data. Multiple window sizes, features, and classifiers are assessed. An in-depth analysis of the classification results is performed to better understand the strengths and weaknesses of the classifier and feature set.

One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC.

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multiscale extended local binary patterns (ELBP) with rotation invariant and uniform mappings to capture robust local micro and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale.

We propose a new sparse coding technique based on the power mean of phase-invariant cosine distances. Our approach is a generalization of sparse filtering and K-hyperlines clustering. It offers a better sparsity enforcer than the L1/L2 norm ratio that is typically used in sparse filtering. At the same time, the proposed approach scales better than the clustering counter parts for high-dimensional input. Our algorithm fully exploits the prior information obtained by preprocessing the observed data with whitening via an efficient row-wise decoupling scheme.

In this paper, a Blind Speech Separation (BSS) technique is introduced based on Independent Component Analysis (ICA) for underdetermined single microphone case. In general, ICA uses noisy speech from at least two microphones to separate speech and noise. But ICA fails to separate when only one stream of noisy speech is available. We use Log Spectral Magnitude Estimator based on Minimum Mean Square Error (LogMMSE) as a non-linear estimation technique to estimate the speech spectrum, which is used as the other input to ICA, with the noisy speech.